With the increasing adoption of multiprocessor systems, many of which include multiple processors each having multiple cores, parallel programming is becoming more attractive. Specifically, parallel programming can be used to optimally separate independent tasks into processes to be run on the independent cores of one or more processors of the system.
To effect such parallel processing, various parallel processing languages exist. However, such parallel languages can be difficult to use. Furthermore, in such languages it can be difficult to perform code analysis, debugging, error checking and so forth. Thus many existing parallel computing languages and environments can be difficult to use, limiting their acceptance among users. Accordingly, although hardware platforms exist to handle parallel programming with coarse-grained processing elements, it can be challenging to perform procedure-level parallel computations on such platforms. Furthermore, when executing parallel programs on such systems, there can be inefficient usage of resources, leading to less than optimal processing, as well as increased power consumption.